k-means clustering resulted in four clusters: a hepatic steatosis cluster cluster 1 , a pancreatic steatosis cluster cluster 2 , a trunk myosteatosis cluster cluster 3 , and a steatopenia cluster cluster 4.

Continuous data are expressed as median interquartile range. Fisher exact test and Wilcoxon rank sum tests were used to compare noncase subjects with case subjects in Japan. In the unadjusted analysis, HRs for T2D incidence in all three clusters were greater than the reference value of 1.

After adjustment for age, sex, alcohol intake, current smoking, and muscle area, the associations of steatosis with T2D were still substantial for all three clusters. Results of pairwise comparisons are shown in Supplementary Table 3.

Model 1: adjustment for age, sex, alcohol intake, current smoking, and muscle area. Model 2: adjustment for age, sex, alcohol intake, current smoking, muscle area, and BMI.

Model 3: adjustment for age, sex, alcohol intake, current smoking, muscle area, BMI, systolic blood pressure, diastolic blood pressure, triglycerides, HDL cholesterol, LDL cholesterol, antihypertensive drugs, and lipid-lowering drugs.

With the steatopenia cluster as the reference, glycemic traits of the other three fat distribution clusters are shown in Table 3. Results of pairwise comparisons are shown in Supplementary Table 5.

Insulin sensitivity and secretion are in arbitrary units. P values were calculated from Wilcoxon rank sum tests comparing the steatopenia cluster with the other clusters. Five interactions were found Supplementary Table 6. All four fat compartments were associated with lower aerobic capacity, with the largest effect size observed for muscle fat.

Individuals without diabetes had a highly heterogenous distribution of fat in the liver, pancreas, skeletal muscle, and visceral areas.

Independently applying data-driven partitioning procedures to two cohorts, we identified four patterns four clusters of fat distribution: a hepatic steatosis cluster, a pancreatic steatosis cluster, a trunk myosteatosis cluster, and a steatopenia cluster.

Compared with the individuals who had low levels of fat in all areas studied i. The distribution of T2D risk among clusters in one cohort was similar to the distribution of glycemia among clusters in the other cohort.

Insulin sensitivity and insulin secretion differed across clusters, which indicates the pathophysiologic contributions of each fat distribution pattern to T2D risk Supplementary Fig. Our results are consistent with those of previous studies: individuals with a high amount of visceral fat and liver fat had a high risk of T2D.

Both visceral fat at baseline and its increase over time were strongly linked to high incidence of T2D 3. In a meta-analysis, liver fat was found to be associated with a twofold higher risk of T2D Our present study confirmed the well-established associations of visceral fat and liver fat with insulin resistance.

Two longitudinal studies detected associations of pancreas fat accumulation with increased risk of T2D 7 , The underlying mechanism is thought to be unfavorable effects of this local fat accumulation on pancreatic insulin secretion 8 , However, pancreas fat is not always detrimental for insulin secretion.

In previous MRI and pathological studies, the association between pancreas fat and insulin secretion impairment was found in individuals with high genetic risk for diabetes but not in those with low genetic risk.

Especially, the genetic risk related to insulin resistance and liver lipid metabolism modulated the relationship between pancreas fat and insulin secretion All of these findings show how the effect of pancreas fat on T2D can be modified by many factors, including genetic risk, metabolic state, and other interacting fat compartments 7 , 8.

Coculture models suggest the presence of a complex organ-organ cross talk modulating insulin secretion 16 , k-means clustering revealed a fat distribution pattern that might fuel such a detrimental interorgan cross talk.

Specifically, individuals in the pancreatic steatosis cluster had lower insulin secretion than expected for their degree of insulin resistance. The hypothesis that pancreatic fat exerts its detrimental effects in combination with other factors is further supported by interactions between fat in the pancreas and in the other tested compartments in terms of glycemia and diabetes risk.

One interesting finding of our current analysis is the contribution of muscle fat to the fat distribution patterns that are associated with T2D risk. The relations among muscle fat accumulation, insulin resistance, and T2D are complex While findings of several cross-sectional analyses suggested that muscle fat can be a risk factor for insulin resistance and T2D 9 , 10 , 13 , it is well-known that fat also accumulates in the muscle of athletes who are very insulin sensitive 13 , Most prior studies evaluated muscle fat in the lower extremities, but here we quantified fat in trunk muscle In concert with fat at other locations, fat in trunk muscle appears to link to T2D risk via muscle and systemic insulin resistance In a few previous studies investigators have already looked at lower-extremity muscle fat when analyzing body fat distribution patterns and T2D.

Miljkovic et al. They showed that liver fat and muscle fat were associated with concurrent T2D. Unlike in the current study, in that study incident T2D was not evaluated and pancreas fat was not measured.

In another recent study 42 , subgroups defined by fat accumulation were identified and were found to be associated with T2D, but that study was also cross-sectional and pancreas fat was not evaluated. Besides comprehensively investigating multiple fat compartments that are known to affect T2D pathogenesis, we aimed to address organ-organ interplay with our clustering approach 16 , This approach bore fruit, with the finding of interactions between fat compartments for glycemia and T2D risk.

Furthermore, the clusters identified in this study had specific constellations of fat distribution and were strongly linked to T2D risk, likely via differences in insulin sensitivity and insulin secretion.

Further studies are warranted to uncover the detailed mechanisms of interplay among fat in different locations. One limitation of this study is the fact that the cohorts were not population based, so they might not reflect the general population. Moreover, we cannot exclude that fat accumulation in the analyzed trunk muscle behaves differently compared with other muscle compartments.

In conclusion, using information on patterns of fat distribution, we identified four distinct groups of individuals. Of note, the pattern of fat distribution was strongly associated with insulin sensitivity, with insulin secretion, and with the likelihood of future T2D.

Unlike separately investigating fat in each location, this new approach provides information on the interplay of excess fat in different locations.

Our findings underline the importance of body fat distribution rather than general adiposity. They can provide a basis for more individualized approaches to preventing and treating T2D. The authors thank Keita Numata from the System Development Section, Keijinkai Maruyama Clinic, and Kunihiko Hayashi, Hiromitsu Yonezawa, Eiji Kazuta, Kimihiro Saito, Keiko Takasaki, and Miyuki Yoshioka from the Department of Radiology, Keijinkai Maruyama Clinic.

For comments and suggestions on an earlier version of the manuscript, the authors acknowledge the assistance of Joseph Green Graduate School of Medicine, University of Tokyo. Duality of Interest.

reports lecture fees from Novo Nordisk and Sanofi. served on an advisory board for Akcea Therapeutics, Daiichi Sankyo, Sanofi, and Novo Nordisk. reports research grants from Boehringer Ingelheim and Sanofi both to the University Hospital Tübingen and lecture fees from Amryt, Novo Nordisk, and Boehringer Ingelheim.

He also served on an advisory board for Boehringer Ingelheim. No other potential conflicts of interest relevant to this article were reported. Author Contributions. designed the study. collected data. wrote the draft of the manuscript.

analyzed data. reviewed the manuscript, made critical revisions, and approved the manuscript before submission. for the Japanese data and R. and M. for the German data are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this study were presented in abstract form at the 82nd Scientific Sessions of the American Diabetes Association, New Orleans, LA, 3—7 June Sign In or Create an Account. Search Dropdown Menu. header search search input Search input auto suggest.

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Obesity Studies June 20 Fat Distribution Patterns and Future Type 2 Diabetes Hajime Yamazaki Hajime Yamazaki. Corresponding author: Hajime Yamazaki, yamazaki-myz umin. This Site. Google Scholar. Shinichi Tauchi ; Shinichi Tauchi. Jürgen Machann ; Jürgen Machann. Tobias Haueise ; Tobias Haueise.

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Figure 1. View large Download slide. Table 1 Baseline characteristics. Age years 51 43—59 54 47—59 0. View Large. Cluster 1: hepatic steatosis. Cluster 2: pancreatic steatosis.

Cluster 3: trunk myosteatosis. Cluster 4: steatopenia. Subcohort contributed equally. Search ADS. Causes, consequences, and treatment of metabolically unhealthy fat distribution.

Change in visceral adiposity independently predicts a greater risk of developing type 2 diabetes over 10 years in Japanese Americans. Independent association between improvement of nonalcoholic fatty liver disease and reduced incidence of type 2 diabetes.

Inverse association between fatty liver at baseline ultrasonography and remission of type 2 diabetes over a 2-year follow-up period. Estimating the effect of liver and pancreas volume and fat content on risk of diabetes: a Mendelian randomization study. Longitudinal association of fatty pancreas with the incidence of type-2 diabetes in lean individuals: a 6-year computed tomography-based cohort study.

Myosteatosis in the context of skeletal muscle function deficit: an interdisciplinary workshop at the National Institute on Aging. Hepatic and skeletal muscle adiposity are associated with diabetes independent of visceral adiposity in nonobese African-Caribbean men.

Cardiovascular and metabolic heterogeneity of obesity: clinical challenges and implications for management. Nonalcoholic fatty liver disease and risk of incident type 2 diabetes: a meta-analysis.

Mechanisms in endocrinology: skeletal muscle lipotoxicity in insulin resistance and type 2 diabetes: a causal mechanism or an innocent bystander?

Metabolic crosstalk between fatty pancreas and fatty liver: effects on local inflammation and insulin secretion. Pancreatic steatosis associates with impaired insulin secretion in genetically predisposed individuals. Fatty pancreas is independently associated with subsequent diabetes mellitus development: a year prospective cohort study.

Quantitative assessment of pancreatic fat by using unenhanced CT: pathologic correlation and clinical implications. Finally, this is a prospective, observational study, which only enables hypothesis generation regarding prediction of regional body fat accumulation by EF and cannot provide mechanistic insight.

Taking into account that visceral fat is associated with increased cardiovascular risk, constituting a potent mediator of unfavourable metabolic profiles, assessment of eating behaviour that may modulate fat distribution provide significant clinical tool with potential therapeutic implications.

Whether meal patterns may serve as a behavioural prevention strategy to alter or prevent adverse regional adiposity patterns should be evaluated in future interventional trials. Determinants and consequences of obesity.

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Advanced Search. Search Menu. Article Navigation. Close mobile search navigation Article Navigation. Volume Article Contents Abstract. Materials and methods. Journal Article. Eating frequency predicts changes in regional body fat distribution in healthy adults.

G Georgiopoulos , G Georgiopoulos. From the Vascular Laboratory, Department of Clinical Therapeutics, Alexandra University Hospital, 80 V. Sofias str, Athens, Greece.

Oxford Academic. K Κaratzi. Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, 70 El Venizelou str, Athens, Greece.

M Yannakoulia. E Georgousopoulou. E Efthimiou. A Mareti. I Bakogianni. Department of Food Science and Human Nutrition, Agricultural University of Athens, Athens, Greece. A Mitrakou. C Papamichael. K Stamatelopoulos. Georgiopoulos and K.

Karatzi contributed equally to this work. Revision received:. PDF Split View Views. Select Format Select format.

Abdominal sagittal diameter derived from CT or MRI images has also been used to determine abdominal FD [ 11 ]. Applying CT scans in volunteers, Lemieux et al proposed cut-off values corresponding to an accumulation of visceral adipose tissue of cm 2 , which is strongly related to metabolic disorders: WHR of 0.

It is noteworthy that, despite continuous technological advances in the measurement of FD, Lemieux et al have suggested the use of simultaneous measurement of WC and fasting triacylglycerol hypertriglyceridaemic waist as a simple screening tool to identify men characterised by the atherogenic metabolic triad hyperinsulinaemia, elevated apolipoprotein B [ApoB], small, dense LDL particles and at high risk for coronary artery disease [ 17 ].

Together with genetic factors, the main predictors of visceral fat and FD are age, sex, total body fat content and energy balance [ 11 ]. Visceral fat mass increases with age independently of total body fat mass, but this is more pronounced in men than in women [ 11 ].

In this context, it has to be mentioned that sex hormones may modulate adipose tissue accumulation in a depot-specific manner.

Alterations in endocrine signalling may also contribute to changes in FD. Moreover, visceral fat mass in men is negatively associated with testosterone and sex-hormone binding globulin SHBG levels [ 20 ].

Despite facilitating the intra-abdominal accumulation of fat, a positive energy balance does not appear to be a major determinant of visceral fat mass. In contrast, there is good evidence that body weight loss results in an over-proportional reduction of the visceral fat mass [ 22 ], which might, at least in part, be explained by higher lipolytic capacity of visceral compared with subcutaneous fat [ 11 ].

In addition, it is likely that environmental factors either directly, or in genetically susceptible individuals, contribute to individual differences in FD Fig. For example, data from rodent studies suggest that food contaminants, particularly those with endocrine signalling capacities, may also cause ectopic fat accumulation in the liver and visceral fat depots [ 23 ].

There is also good evidence in humans that sugar-sweetened beverages promote the accumulation of visceral adiposity [ 24 ]. Moreover, as suggested by Björntorp, stress mediated by psychosocial and socioeconomic handicaps, depressive and anxiety traits, alcohol and smoking may lead to neuroendocrine perturbations followed by abdominal obesity with its associated comorbidities [ 25 ].

Genetics of FD: strategies to identify genes involved in fat distribution and potential mechanisms contributing to variability in FD.

GWAS provide a major tool for identification of novel genes associated with FD measures, such as WHR. Candidate gene strategies rely on the investigation of genes with fat depot-specific mRNA expression.

In addition, genes known to be involved in the pathophysiology of other forms of altered fat distribution, such as lipodystrophies, are potential candidates e.

Developmental genes represent a unique group of genes that is not only supported by their physiological role but also by GWAS. CB1R is also known as CNR1. There is good evidence that not only obesity but also FD is controlled by genetic factors, and that this is independent of BMI and overall obesity [ 26 , 27 ].

In one of the pioneering works in this field, Bouchard et al showed in identical twins that within-pair similarity was particularly evident for changes in regional FD and amount of abdominal visceral fat, with significantly greater variance among pairs than within pairs, thus strongly suggesting the involvement of genetic factors [ 21 ].

These data suggested a strong genetic influence on the familial aggregation in abdominal fat, independent of total body fat mass, and clearly indicated that genetic factors seem to have a greater effect on abdominal visceral fat than on abdominal subcutaneous adipose tissue.

Since some individuals are genetically predisposed to store abdominal fat in the visceral rather than in the subcutaneous depot, the above-mentioned studies also implied that these individuals are at higher genetic risk of manifest metabolic complications associated with visceral obesity [ 31 ].

Conditions such as steatopygia and lipodystrophies also support the role of genetics in FD. There are obvious differences in body FD as humans gain or lose weight.

This is extremely profound in certain ethnic groups such as the Khoikhoi previously known as Hottentots in southern Africa, whose women show excessive accumulation of fat in the buttocks [ 34 ].

Lipodystrophies with abnormal regional fat deposition provide further convincing evidence for the role of genetics in FD [ 35 ]. For instance, for congenital generalised lipodystrophy, which is characterised by a partial or complete loss of any adipose tissue, mutations in four genes, AGPAT2 , BSCL2 , CAV1 and PTRF , leading to disturbances in either lipid storage AGPAT2 or lipid homoeostasis CAV1 , PTRF , BSCL2 have been postulated to be causative [ 36 — 39 ].

Patients with Dunnigan-type familial partial lipodystrophy suffer post puberty from regional and progressive adipocyte degeneration, which is often accompanied by profound insulin resistance and diabetes [ 42 ].

It has been demonstrated in mice that, rather than involving a loss of fat, the major mechanism contributing to the lack of fat accumulation is likely to be an altered renewal capacity of the adipose tissue, which could be attributed to the disturbed differentiation of pre-adipocytes into functional adipocytes [ 43 ].

Interestingly, common variants in LMNA have been shown to contribute to the polygenic background of type 2 diabetes and obesity [ 44 — 46 ], making LMNA a plausible candidate for involvement in the pathophysiology of visceral obesity Fig.

Familial multiple lipomatosis is another condition of altered FD that is characterised by the presence of multiple lipomas on the body. Although an autosomal-dominant inheritance has been proposed [ 47 ], information on the genetic background of lipomatosis is sparse and research has so far focused on HMGA2 and its fusion partners LPP and LHFP [ 48 — 50 ].

Of note, transgenic mice expressing truncated Hmga2 still retaining the three AT-hook domains are characterised by a giant phenotype and hyperplasia of white adipose tissue [ 51 ] whereas, on the other hand, HMGA2 knockout mice present a pygmy phenotype with hypoplasia of white adipose tissue [ 52 ].

Likewise, a lack of HMGA2 impairs lineage commitment of stem cells toward pre-adipocytes, further supporting the role of HMGA2 in adipogenesis [ 53 ].

The classical approach to examining the heterogeneity of adipose tissue is based on comparisons of protein and gene function and expression between the visceral and subcutaneous fat depots. Differential gene expression between visceral and subcutaneous adipose tissue points to genetic heterogeneity and, therefore, its investigation represents a promising path to reveal candidate genes involved in the regulation of FD.

Indeed, there are numerous genes with differential expression between visceral and subcutaneous adipose tissue, such as ADRB3 [ 54 ], APOB [ 55 ], GR also known as NR3C1 [ 56 ], LPL [ 57 ], PAI1 [ 58 ], RBP4 [ 59 ], LEP [ 60 ], IL6 [ 61 ], AGT [ 60 ] or PPARG [ 62 ] Fig.

It has been postulated that genetic variants in these genes may contribute to ectopic visceral storage [ 63 ]. Moreover, many of these genes, notably ADRB3 , APOB , LPL , RBP4 , LEP , IL6 , APM1 and PPARG , are not only differentially expressed in various fat depots, but are also associated with traits related to obesity, such as insulin resistance or adipokine levels.

These findings clearly indicate potential functionality of the identified polymorphisms in the regulation of FD. It has to be acknowledged, however, that in general, early candidate gene studies were mostly underpowered and the genotype—phenotype associations would not have withstood the currently accepted genome-wide statistical significance levels.

Nevertheless, they undoubtedly deserve attention as they may still represent very promising targets contributing to a better understanding of the complex aetiology of obesity-related complications, and might even pave the way for novel treatment strategies in metabolic disorders. For example, given its biological role in metabolism, PPARG the gene encoding peroxisome proliferator-activated receptor γ is one of the most prominent candidates for involvement in modulating FD.

Treatment of type 2 diabetes with thiazolidinediones, which activate PPARG selectively, increases fat partitioning to the subcutaneous adipose depot [ 68 ] and may also reduce visceral fat volume [ 69 ].

Therefore, it may not be surprising that the genetic variant predicting a ProGln change in PPARG has also been investigated intensively in relation to FD and was not only found in extremely obese subjects but also shown to promote adipocyte differentiation [ 70 ].

In contrast, the Pro12Ala variant in PPARG is associated with lower BMI, better insulin sensitivity and reduced risk of type 2 diabetes [ 63 ]. Although a significant PPARG gene—sex interaction was observed in the modulation of BMI, fat mass and blood pressure for Pro12Ala, it was also associated with WC independently of BMI and sex [ 71 ].

Consistently, the polymorphism was associated with WHR and visceral and subcutaneous fat mass in Korean women, although the data suggested that the gene has a larger impact on subcutaneous than visceral adipose tissue [ 72 ].

As sirtuin 1 SIRT1 is an important regulator of energy metabolism through its impact on glucose and lipid metabolism, genetic studies have been performed to test the effects of genetic variation in SIRT1 on adiposity.

A Belgian case—control association study involving 1, obese patients and lean controls suggested that genetic variants of SIRT1 increase the risk for obesity, and that the SIRT1 genotype correlates with visceral obesity variables WC, WHR and visceral and total abdominal fat in obese men [ 73 ].

One of the earliest studies revealed an association between rs and WC in individuals of Indian-Asian or European ancestry [ 74 ]. The SNPs have been mapped near MC4R , which is known to be predominantly involved in monogenic obesity [ 74 ]. Since then, MC4R has remained one of the major WC-associated loci, conferring a relatively large effect size of 0.

In addition, a few other loci related to WC, including the neurexin 3 gene NRXN3 , have been discovered [ 75 ]. Since the effect of the associated variant was markedly attenuated when adjusting for BMI, it is likely that NRXN3 is involved in regulating overall obesity rather than WC [ 75 ], which is in line with previous studies showing NRXN3 , to be related to obesity and BMI [ 76 ].

The first meta-analysis of GWAS relating to WC and WHR was conducted by Lindgren et al and suggested a role for genetic factors in the regulation of both WC and WHR [ 77 ].

Genetic variants within TFAP2B and near MSRA were strongly associated with visceral fat accumulation WC. In line with the Lindgren meta-analysis, a recent study conducted with 32 GWAS for WHR adjusted for BMI up to 77, participants , and following up 16 loci in an additional 29 studies up to , participants , uncovered 13 loci associated with WHR RSPO3 , VEGFA , TBX15—WARS2 , NFE2L3 , GRB14—COBLL1 , DNM3—PIGC , ITPR2—SSPN , LY86 , HOXC13 , ADAMTS9 , ZNRF3—KREMEN1 , NISCH—STAB1 , CPEB4 and confirmed the known association signal at LYPLAL1 , with effect sizes reaching 0.

A recent GWAS including up to , individuals of European ancestry replicated associations with WHR for RSPO3 , LY86 , LYPLAL1 and COBLL1 [ 78 ]. Altogether, the GWAS findings indicate a strong genetic background for WHR regulation, independently of overall obesity.

Sex-specific effect sizes of WHR-associated loci: effect sizes of all genome-wide significant WHR loci meta-analysed by 1 Heid et al [ 30 ] and by 2 Randall et al [ 84 ]. The data are ordered by effect sizes in women and reported for the combined stages of analyses. In addition to GWAS for WHR, several studies used more precise measures of FD, such as visceral and subcutaneous fat area measured by CT [ 79 , 80 ].

These GWAS revealed additional variants implying the value of more accurate measurements in unravelling novel polymorphisms contributing to the genetic control of FD. In particular, Fox et al provided strong evidence for an association of a novel locus with visceral adipose tissue near THNSL2 in women [ 80 ].

Moreover, using the ratio of visceral to subcutaneous adipose tissue, significant associations were replicated for seven of the previously reported WHR loci after adjusting for BMI [ 30 , 80 ]. Given the known limitations of WHR as a measure of FD, particularly based on the fact that, for a given WHR value, there may be large variation in the level of abdominal visceral adipose tissue, the data from Fox et al clearly demonstrate the need for including more accurate phenotypes of regional FD in future genetic analyses.

Although most of the previous GWAS were conducted in cohorts of European ancestry, recent studies have replicated the previously identified associations in various ethnic populations [ 81 , 82 ].

While confirming six of the 14 loci described above for WHR TBX15—WARS2 , GRB14 , ADAMTS9 , LY86 , RSPO3 , ITPR2—SSPN , two novel regions have been shown to associate significantly with WC and WHR LHX2 and RREB1 , respectively; both adjusted for BMI in individuals of African ancestry [ 81 ].

Furthermore, recent analyses of the 14 WHR loci confirmed the potential role in FD for LYPLAL1 and NISCH in a Japanese population [ 82 ]. It is of note that a recent GWAS identified a novel SNP near TRIP2 associated with pericardial fat [ 83 ].

The variant rs was exclusively associated with pericardial fat in a multi-ethnic survey, without any further evidence of association with visceral adipose tissue or BMI.

The authors provided further evidence for an expression quantitative trait locus eQTL suggesting that the association of the lead variant close to TRIP 2 might be mediated by its altered gene expression [ 83 ]. The fact that seven of the loci identified by Heid et al RSPO3 , VEGFA , GRB14 , LYPLAL1 , ITPR2—SSPN , ADAMTS9 , HOXC13 Fig.

The sexual dimorphism in FD gained further support from a very recent large-scale study comprising , individuals in the initial meta-analysis of GWAS and , individuals in subsequent replication stages [ 84 ]. Specifically, significant sex-related differences were replicated for four of the 14 previously reported WHR-associated loci adjusted for BMI near GRB14—COBLL1 , LYPLAL1—SLC30A10 , VEGFA and ADAMTS9 Fig.

Moreover, three novel loci were identified for WC near MAP3K1 and WHR adjusted for BMI near HSD17B4 and PPARG Fig. The observed sexual dimorphism may not be surprising when considering that sex differences manifest not only in WHR per se but also in the extent of genetic effects influencing variation in body composition.

Although there is a shortage of studies systematically investigating sex differences in the genetic architecture of body composition, Zillikens et al showed that genetic variance was significantly higher in women for waist, hip and WHR in the Erasmus Ruphen Family study, thus suggesting that genes account for more phenotypic variance of FD in women than in men [ 85 ].

The above-mentioned GWAS strengthen the conclusions of the Erasmus Ruphen Family study and clearly imply the tremendous importance of sex-specific analyses in studies aimed at pinpointing the genetic architecture of complex traits such as FD.

Nevertheless, it has to be kept in mind that genetic determinants of body composition may be modulated by sex-specific hormonal, environmental and nutritional factors [ 85 ], which may at least partially explain the observed sex-related differences in genetic effects on FD. In contrast to the variants related to obesity and BMI, which are mostly expressed in the brain, the vast majority of the WHR-related genes identified by GWAS are predominantly expressed in peripheral tissues [ 76 , 86 , 87 ].

However, a functional role could be assigned to only a handful of candidate genes, predominantly involved in adipogenesis TBX15 , early embryonic development HOXC13 , angiogenesis VEGFA , RSPO3 and STAB1 , lipase activity LYPLAL1 or lipid biosynthesis PIGC reviewed in [ 30 ].

With regard to the potential function of the genes identified, and apart from the sex-specific association signals, another important aspect of the GWAS carried out by Heid et al was the identification of eQTLs for six WHR-associated SNPs rs for TBX15 mRNA in omental adipose tissue; rs for AA mRNA, rs for GRB14 mRNA and rs for ZNRF3 mRNA in subcutaneous adipose tissue; rs for PIGC mRNA in lymphocytes; rs for STAB1 mRNA in blood [ 30 ].

At these loci, the WHR-associated SNPs explained the majority of the association between the most significant eQTL and the gene transcript. Moreover, mRNA of the five potential FD-related genes RSPO3 , TBX15 , ITPR2 , WARS2 and STAB1 was differentially expressed between gluteal and abdominal subcutaneous adipose tissue.

In follow-up studies, these data could be strengthened by demonstrating fat depot-specific differences in mRNA expression between subcutaneous and visceral adipose tissue for all six genes mapped within the reported eQTLs [ 88 ].

Finally, the rs T-allele was nominally associated with increased GRB14 subcutaneous mRNA expression, suggesting that the association with WHR might be mediated by the SNP effects on mRNA expression levels.

GRB14 appears to be an appealing gene as it binds to the insulin receptor and its expression is enhanced in patients with type 2 diabetes. Consequently, as postulated by Holt et al, it could either be the case that small differences in numerous adipose depots may lead to a significant overall difference in fat mass or, alternatively, that there may be depot-specific differences in fat accumulation with no change observed for the epididymal depot [ 91 ].

Fat depot-specific expression of developmental genes provides further support for the strong genetic background of FD [ 92 ]. It has been observed in both rodents and humans that visceral adipose tissue is characterised by higher mRNA levels of HoxA5 , HoxA4 , HoxC8 , Gpc4 and Nr2f1 , whereas subcutaneous fat has higher levels of HoxA10 , HoxC9 , Twist1 , Tbx15 , Shox2 , En1 and Sfpr2.

Even more importantly, such variability in gene expression is also found in pre-adipocytes derived from different fat depots in rodents and humans, and appears to be intrinsic, since it persists during in vitro culture and differentiation [ 92 , 93 ].

This may suggest that different mesodermal regions might give rise to precursors in different adipose depots, and might so contribute to biological differences between visceral and subcutaneous adipose tissue.

In support of this, Gesta et al have shown that mRNA expression profiles of Tbx15 , Gpc4 and HoxA5 not only differ between various adipose depots but also strongly correlate with BMI, WHR or visceral fat mass and subsequent metabolic alterations in mice as well as humans, suggesting that genetic differences in regulation of the development and differentiation of adipocytes could at least partially explain the development of visceral obesity [ 92 , 94 ].

It is noteworthy, however, that depot-specific differences have been observed even within subcutaneous adipose tissue, as demonstrated recently by Karastergiou et al, who investigated depot- and sex-dependent differences in gene expression in human abdominal and gluteal subcutaneous adipose tissue [ 95 ].

There was again strong evidence for differential regulation of mRNA expression of homeobox genes in both sexes, implying that developmentally programmed differences may contribute to the distinct phenotypic characteristics of peripheral fat [ 95 ].

Consistently, a unique expression pattern of developmental genes has been previously described by Yamamoto et al for Shox2 , En1 , Tbx15 , Hoxa5 , Hoxc8 and Hoxc9 in several subcutaneous and intra-abdominal white and brown adipose tissue depots in mice under obese and in fasting conditions Fig.

With regard to gene function, it has been shown very recently that SHOX2 , whose expression levels in human subcutaneous adipose tissue positively correlate with visceral obesity, regulates lipolysis via increasing ADRB3 expression, thus suggesting its role in adipocyte biology [ 97 ].

It should be noted that, despite recent advances in the field of high-throughput genetic analyses resulting in a number of novel polymorphisms associated with WHR, these polymorphisms can only explain a small proportion of phenotypic variance and genetic heritability in FD [ 30 ].

Therefore, other players such as non-coding RNA or DNA methylation need to be acknowledged as possible regulators of FD Fig. Epigenetic modifications, such as DNA or histone methylation, modify long-term gene expression and seem to provide plausible mechanisms for adapting the genome to environmental circumstances.

It has been shown that nutritional oscillations in certain developmental periods of life may increase susceptibility to overweight and related diseases. Perinatal programming of the genome based on prenatal and neonatal overfeeding contributes to obesity and diabetes in later life [ 98 ].

It is also increasingly appreciated that there is an association between maternal nutrition during pregnancy and intrauterine development of fetal body composition and subsequently FD later in life [ 99 ].

More importantly, body composition and adverse FD may be modifiable via nutritional intervention in the mother [ 99 ]. As mentioned above, the strong adipose tissue-specific expression patterns of genes playing a fundamental role in early development were strikingly found to be preserved from one pre-adipocyte to the next over several generations [ 93 , ], implying the existence of yet unknown mechanisms maintaining the expression profiles over time [ ].

This is not only supported by demonstrating that white and brown adipose tissue originate from independent precursor cells [ ] but also by showing distinct methylation profiles for white and brown pre-adipocytes [ ]. In a genome-wide methylation analysis of eight different adipose depots in three pig breeds living within comparable environments, but displaying distinct fat levels, Li et al investigated the systematic association between anatomical location-specific DNA methylation status of different adipose depots and obesity-related phenotypes [ ].

Using methylated DNA immunoprecipitation sequencing, the authors showed that, compared with subcutaneous adipose tissue, visceral and intermuscular adipose tissue, which are the metabolic risk factors of obesity, were primarily associated with impaired inflammatory and immune responses.

By presenting functionally relevant methylation differences between different adipose depots, the study supports the role of epigenetics in the regulation of FD. Epigenetic studies on animal models are now being complemented by human studies, which bring further evidence for the potential role of epigenetics in the pathophysiology of adverse FD.

For instance, a recent study by Huang et al described a positive correlation between IGF2—H19 DNA methylation levels and ultrasound-derived measures of subcutaneous fat thickness in young adults [ ].

Furthermore, DNA methylation levels at the LEP promoter were shown to be related to its tissue distribution [ ]. Undoubtedly, and regardless of forms of altered FD, fat deposition is strongly determined by genetic factors. Whereas specific forms of disturbed FD, such as lipodystrophies, can be clearly assigned to individual genetic mutations, other forms, such as visceral obesity, appear to be of a polygenic nature and further influenced by environmental factors.

Although genes involved in the pathophysiology of monogenic forms of altered FD may be attractive candidates in studies aimed at investigating common genetic variation and its effects on FD as has been demonstrated for LMNA variants associated with type 2 diabetes and obesity , recent GWAS on measures of FD proved to be the most efficient tool in identifying genetic loci potentially harbouring genes controlling FD Fig.

It is of note that many of the WHR-associated loci have also shown associations in GWAS for metabolic traits such as fasting glucose, insulin, adiponectin levels and BMI, and with diseases such as type 2 diabetes, hypertension and coronary heart disease ESM Table 1 , so further supporting the suggestion that individuals genetically predisposed to store fat in the visceral rather than the subcutaneous depot are at higher risk of developing various metabolic complications.

The challenge is now to understand the biological processes controlled by these genes leading to altered FD. For example, considering the fact that dysfunctional adipose tissue that is unable to expand through hyperplasia will lead to visceral accumulation and ectopic fat deposition, it might be hypothesised that some individuals with genetically determined dysfunctional subcutaneous adipose tissue may be more prone to storing variable amounts of fat in other ectopic depots e.

liver, heart, muscle, or around large vessels depending on variation in other sets of genes Fig. a Functional adipose tissue expansion through hyperplasia to cover the need to store excess energy. b Dysfunctional adipose tissue unable to expand through hyperplasia will lead to visceral adipose tissue accumulation and to ectopic fat deposition.

An excess in body fat arises in most cases from a mixture of adverse lifestyle components e. low physical activity, hyper-energetic nutrition and genetic susceptibility. In addition, expansion capacity of subcutaneous adipose tissue and storage of energy in various ectopic fat depots might be modulated by different gene sets.

Thus, some individuals with genetically determined dysfunctional subcutaneous adipose tissue may be more prone to store variable amounts of fat in other ectopic depots e. liver, heart, muscle or around large vessels depending upon variation in other sets of genes.

SC subcutaneous; VIS visceral. In conclusion, a better knowledge of the function of FD genes will be crucial for understanding the complex aetiology of obesity-related complications and might even pave novel paths for treatment strategies for metabolic disorders such as diabetes.

In addition, more accurate methods, including cardiometabolic imaging, for assessment of FD will be required to promote our knowledge in this field. Van Gaal LF, Mertens IL, De Block CE Mechanisms linking obesity with cardiovascular disease. Nature — PubMed Google Scholar. Reaven GM Importance of identifying the overweight patient who will benefit the most by losing weight.

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Hepatology — Please turn on JavaScript and try again. Main Content. Important Phone Numbers. Top of the page. Current as of: August 25, Home About MyHealth. ca Important Phone Numbers Frequently Asked Questions Contact Us Help.

About MyHealth. feedback myhealth. Include Images Large Print. In conclusion, excess accumulation of either visceral abdominal or muscle AT is associated with a higher prevalence of metabolic syndrome in older adults, particularly in those who are of normal body weight.

This suggests that practitioners should not discount the risk of metabolic syndrome in their older patients entirely on the basis of body weight or BMI. Indeed, generalized body composition, in terms of both BMI and the proportion of body fat, does not clearly distinguish older subjects with the metabolic syndrome.

Moreover, racial differences in the various components of the metabolic syndrome provide strong evidence that the cause of the syndrome likely varies in blacks and whites. Thus, the development of a treatment for the metabolic syndrome as a unifying disorder is likely to be complex.

Correspondence: Bret H. Goodpaster, PhD, Department of Medicine, North MUH, University of Pittsburgh Medical Center, Pittsburgh, PA bgood pitt. Dr Goodpaster was supported by grant KAG from the National Institute on Aging, National Institutes of Health.

full text icon Full Text. Download PDF Top of Article Abstract Methods Results Comment Article Information References. Figure 1. View Large Download. Table 1. Characteristics of Men and Women With and Without Metabolic Syndrome.

Regional Fat Distribution According to Metabolic Syndrome Status. Abdominal AT in Men and Women With and Without Metabolic Syndrome According to a Revised Definition Omitting Waist Circumference.

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Epigenetics — Genome Res — Download references. DS is funded by the Boehringer Ingelheim Foundation. YB and PK are supported by the IFB AdiposityDiseases K50D and K to YB; K and K to PK.

IFB AdiposityDiseases is funded by the Federal Ministry of Education and Research BMBF , Germany, FKZ: 01EO DS, YB, MB and PK were responsible for the conception and design of the manuscript, drafting the manuscript, revising it critically for intellectual content and approving the final version.

Integrated Research and Treatment Center IFB AdiposityDiseases, University of Leipzig, Liebigstr. Department of Medicine, University of Leipzig, Leipzig, Germany.

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Donath Assessment of Whole Body Composition with Dual Energy X-ray Absorptiometry. Subsequent analysis showed that the combination of F1 and F2 which was statistically independent of G1 G2 was nearly as correlated with body mass index BMI as was G1 0. Moreover mean G2 did not differ between treated diabetics and non-diabetics at entry in the study whereas mean G1 was significantly increased in the former group.

Linear correlation coefficients of G2 with systolic blood pressure total cholesterol and triglycerides measured at entry were low 0.